National forest roads allow access to public lands providing connections to natural and cultural heritage. Planning processes that address potential road closures or conversions can be highly contentious. Public participatory GIS (PPGIS) has been used as a tool to gather information for environmental planning and decision-making. Our PPGIS approach in a national forest in Washington (USA) incorporated workshops and online engagement with 1,810 participants to gather public input for sustainable roads planning. We identified the most important forest destinations and developed an analytical framework for assessing forest roads based on the density and diversity of use. In this paper, we summarize our PPGIS process and identify challenges faced in the application of socio-spatial data. A comparative analysis of road planning in other forests further highlights challenges in incorporating public use data. While the PPGIS process was valued for relationship-building, it is less evident how directly the socio-spatial data informed outcomes.

Xverum’s Urban Planning Data is a comprehensive dataset of 230M+ verified locations, offering insights into commercial real estate, property trends, and urban development. Covering 5000 categories, our dataset supports real estate investors, urban planners, and policymakers in making data-driven decisions for infrastructure development, property market analysis, and zoning regulations.

With regular updates and continuous POI discovery, Xverum ensures your real estate and urban planning models have the latest property and commercial development data. Delivered in bulk via S3 Bucket or cloud storage, our dataset is ideal for GIS applications, market research, and smart city development.

🔥 Key Features:

Extensive Coverage for Urban Planning & Real Estate: ✅ 230M+ locations worldwide, spanning 5000 categories. ✅ Covers retail, office, industrial, hospitality, and mixed-use properties.

Geographic & Property Market Data: ✅ Latitude & longitude coordinates for precise mapping & real estate valuation. ✅ Property classifications, including commercial & mixed-use assets. ✅ Country, state, city, and postal code classifications for regional analysis.

Comprehensive Real Estate & Property Data: ✅ Property metadata, including location type, size, and market value insights. ✅ Business & commercial property listings for competitive analysis. ✅ Zoning data & regulatory insights for urban expansion & infrastructure planning.

Optimized for Real Estate & Urban Development: ✅ Supports market research, investment analysis & infrastructure development. ✅ Enhances real estate forecasting & planning applications. ✅ Provides in-depth insights for land use and smart city initiatives.

Bulk Data Delivery (NO API): ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in a structured format (.json) for seamless integration.

🏆 Primary Use Cases:

Urban Planning & Infrastructure Development 🔹 Optimize land use planning, zoning, and city expansion projects. 🔹 Enhance GIS mapping with real estate & infrastructure data.

Real Estate Market Analysis & Investment Research: 🔹 Track commercial property trends & investment opportunities.

Smart City & Economic Growth Planning: 🔹 Identify high-growth regions for real estate & commercial expansion.

💡 Why Choose Xverum’s Urban Planning Data? - 230M+ Verified Locations – One of the largest & most structured real estate datasets available. - Global Coverage – Spanning 249+ countries, covering all real estate & property sectors. - Regular Updates & New Property Discoveries – Ensuring the highest accuracy. - Comprehensive Geographic & Market Metadata – Coordinates, zoning insights & property classifications. - Bulk Dataset Delivery – Direct access via S3 Bucket or cloud storage. - 100% Compliant – Ethically sourced & legally compliant.

This StoryMap explores the innovative use of imagery in Kaupapa Māori GIS mapping, emphasizing the synergy between traditional Māori knowledge and contemporary geospatial technologies. Key themes include:​Remote Sensing in Environmental Stewardship: Utilizing satellite imagery and aerial photography to monitor natural resources, assess environmental changes, and inform sustainable practices.​Cultural Heritage Preservation: Mapping sacred sites, ancestral lands, and taonga to safeguard Māori heritage and inform cultural revitalization efforts.​Land Use Planning and Development: Employing GIS tools to support iwi and hapū in decision-making processes related to land management, urban planning, and resource allocation.​Community Engagement and Education: Empowering Māori communities through participatory mapping projects, capacity building, and educational initiatives that bridge traditional wisdom with technological advancements.​Through case studies and interactive maps, this StoryMap illustrates the transformative potential of integrating imagery and GIS within Kaupapa Māori frameworks, offering insights and practical applications for indigenous mapping initiatives.

Xverum’s Global GIS & Geospatial Data is a high-precision dataset featuring 230M+ verified points of interest across 249 countries. With rich metadata, structured geographic attributes, and continuous updates, our dataset empowers businesses, researchers, and governments to extract location intelligence and conduct advanced geospatial analysis.

Perfectly suited for GIS systems, mapping tools, and location intelligence platforms, this dataset covers everything from businesses and landmarks to public infrastructure, all classified into over 5000 categories. Whether you're planning urban developments, analyzing territories, or building location-based products, our data delivers unmatched coverage and accuracy.

Key Features: ✅ 230M+ Global POIs Includes commercial, governmental, industrial, and service locations - updated regularly for accurate relevance.

✅ Comprehensive Geographic Coverage Worldwide dataset covering 249 countries, with attributes including latitude, longitude, city, country code, postal code, etc.

✅ Detailed Mapping Metadata Get structured address data, place names, categories, and location, which are ideal for map visualization and geospatial modeling.

✅ Bulk Delivery for GIS Platforms Available in .json - delivered via S3 Bucket or cloud storage for easy integration into ArcGIS, QGIS, Mapbox, and similar systems.

✅ Continuous Discovery & Refresh New POIs added and existing ones refreshed on a regular refresh cycle, ensuring reliable, up-to-date insights.

✅ Compliance & Scalability 100% compliant with global data regulations and scalable for enterprise use across mapping, urban planning, and retail analytics.

Use Cases: 📍 Location Intelligence & Market Analysis Identify high-density commercial zones, assess regional activity, and understand spatial relationships between locations.

🏙️ Urban Planning & Smart City Development Design infrastructure, zoning plans, and accessibility strategies using accurate location-based data.

🗺️ Mapping & Navigation Enrich digital maps with verified business listings, categories, and address-level geographic attributes.

📊 Retail Site Selection & Expansion Analyze proximity to key POIs for smarter retail or franchise placement.

📌 Risk & Catchment Area Assessment Evaluate location clusters for insurance, logistics, or regional outreach strategies.

Why Xverum? ✅ Global Coverage: One of the largest POI geospatial databases on the market ✅ Location Intelligence Ready: Built for GIS platforms and spatial analysis use ✅ Continuously Updated: New POIs discovered and refreshed regularly ✅ Enterprise-Friendly: Scalable, compliant, and customizable ✅ Flexible Delivery: Structured format for smooth data onboarding

Request a free sample and discover how Xverum’s geospatial data can power your mapping, planning, and spatial analysis projects.

The contemporary globalized world characterizes the rapid population growth, its significant concentration in cities, and an increase in the urban population. Currently, many socio-cultural, economic, environmental, and other challenges are arising in modern cities, negatively affecting the state of the urban environment, health, and quality of life. There is a need to study these problems in order to solve them. Urban Green Areas (UGAs) are a part of the social space and a vital part of the urban landscape. They act as an environmental framework of the territory and a factor ensuring a more comfortable environment of human life. This study aims at substantiating the importance of the UGAs, identifying the spatiotemporal dynamics of their functioning, and transforming changes in their infrastructure given the expansion of their functions. This research was carried out as a case study of the second city in Ukraine, Kharkiv. The authors developed and used an original integrated approach using urban remote sensing (URS) and GIS for changes detection to evaluate the current state and monitor spatial transformations of the UGAs. We used several GIS platforms and online resources to overcome the lack of digital cadastre of the thematic municipal area of Kharkiv. This resulted in the present original study. The study analyses the dynamics of the spatial and functional organization of the UGAs according to the Master Plans, plans, maps, and functional zoning of the city for the period from 1867 to 2019. The peripheral green areas became important after the large-scale development of the extensive residential areas during the rapid industrial development in remote districts of the city. They provide opportunities for population recreation near living places. Central UGAs are modern, comprehensively developed clusters with multidisciplinary infrastructure, while the peripheral UGAs are currently being developed. The use of URS/GIS tools in the analysis of the satellite images covering 2000–2020 allowed identifying the factors of the UGAs losses in Kharkiv and finding that UGAs were not expanding and partially shrinking during the study period. It is caused by the intensive construction of the residential neighborhoods, primarily peripheral areas, infrastructure development, and expansion of the city transport network. Nonetheless, some sustainable trends of UGA functioning without more or less significant decrease could be proved as existing in a long-term perspective. The authors analyzed and evaluated changes and expansion of the UGAs functions according to modern social demand. The research value of this is the usage of different approaches, scientific sources, URS/GIS tools to determine the UGAs transformation in the second-largest city in Ukraine (Kharkiv), to expand and update the main functions of UGAs and their role in the population’s recreation. The obtained scientific results can be used to update the following strategies, programs, and development plans of Kharkiv.

Unlock precise, high-quality Map data covering 164M+ verified locations across 220+ countries. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

Xverum’s Point of Interest (POI) Data is a comprehensive dataset containing 230M+ verified locations across 5000 business categories. Our dataset delivers structured geographic data, business attributes, location intelligence, and mapping insights, making it an essential tool for GIS applications, market research, urban planning, and competitive analysis.

With regular updates and continuous POI discovery, Xverum ensures accurate, up-to-date information on businesses, landmarks, retail stores, and more. Delivered in bulk to S3 Bucket and cloud storage, our dataset integrates seamlessly into mapping, geographic information systems, and analytics platforms.

🔥 Key Features:

Extensive POI Coverage: ✅ 230M+ Points of Interest worldwide, covering 5000 business categories. ✅ Includes retail stores, restaurants, corporate offices, landmarks, and service providers.

Geographic & Location Intelligence Data: ✅ Latitude & longitude coordinates for mapping and navigation applications. ✅ Geographic classification, including country, state, city, and postal code. ✅ Business status tracking – Open, temporarily closed, or permanently closed.

Continuous Discovery & Regular Updates: ✅ New POIs continuously added through discovery processes. ✅ Regular updates ensure data accuracy, reflecting new openings and closures.

Rich Business Insights: ✅ Detailed business attributes, including company name, category, and subcategories. ✅ Contact details, including phone number and website (if available). ✅ Consumer review insights, including rating distribution and total number of reviews (additional feature). ✅ Operating hours where available.

Ideal for Mapping & Location Analytics: ✅ Supports geospatial analysis & GIS applications. ✅ Enhances mapping & navigation solutions with structured POI data. ✅ Provides location intelligence for site selection & business expansion strategies.

Bulk Data Delivery (NO API): ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in structured format (.json) for seamless integration.

🏆Primary Use Cases:

Mapping & Geographic Analysis: 🔹 Power GIS platforms & navigation systems with precise POI data. 🔹 Enhance digital maps with accurate business locations & categories.

Retail Expansion & Market Research: 🔹 Identify key business locations & competitors for market analysis. 🔹 Assess brand presence across different industries & geographies.

Business Intelligence & Competitive Analysis: 🔹 Benchmark competitor locations & regional business density. 🔹 Analyze market trends through POI growth & closure tracking.

Smart City & Urban Planning: 🔹 Support public infrastructure projects with accurate POI data. 🔹 Improve accessibility & zoning decisions for government & businesses.

💡 Why Choose Xverum’s POI Data?

• 230M+ Verified POI Records – One of the largest & most detailed location datasets available.
• Global Coverage – POI data from 249+ countries, covering all major business sectors.
• Regular Updates – Ensuring accurate tracking of business openings & closures.
• Comprehensive Geographic & Business Data – Coordinates, addresses, categories, and more.
• Bulk Dataset Delivery – S3 Bucket & cloud storage delivery for full dataset access.
• 100% Compliant – Ethically sourced, privacy-compliant data.

Access Xverum’s 230M+ POI dataset for mapping, geographic analysis, and location intelligence. Request a free sample or contact us to customize your dataset today!

Urban planning is a lengthy and settled process, the results of which usually emerge after several years or even decades. That is why it is necessary for a proper urban design of cities to use parameters that are able to predict and gauge the potential long-term behaviour of urban development.In the tourist towns of the Mediterranean coast, the long-term design is often at odds with the generation of business profits in the short term. This paper presents the results of this phenomenon for an interesting case of a Spanish Mediterranean coastal city created from scratch in the 1960s and turned into a tourist destination today hypertrophied.La Manga del Mar Menor in the Murcia region every year reaches a population of more than 250,000 people during the summer, which is reduced to just a few dozen in winter. This crowded environment with an asymmetric behaviour submits annual progressive impoverishment in its economic return. This questionable profitability is the result of a misguided urban development; its results are analyzed through the evolution of the land market and the resulting urbanization in the last fifty years, with a GIS methodology. (C) 2014 Elsevier Ltd. All rights reserved.

1. Cultural Ecosystem Services (CES) encompass a range of social, cultural and health benefits to local communities, for example recreation, spirituality, a sense of place and local identity. However, these complex and place-specific CES are often overlooked in rapid land management decisions and assessed using broad, top–down approaches. 2. We use the Toolkit for Ecosystem Service Site-based Assessment (TESSA) to examine a novel approach to rapid assessment of local CES provision using inductive, participatory methods. We combined free-listing and participatory geographic information systems (GIS) techniques to quantify and map perceptions of current CES provision of an urban green space. The results were then statistically compared with those of a proposed alternative scenario with the aim to inform future decision-making. 3. By identifying changes in the spatial hotspots of CES in our study area, we revealed a spatially-specific shift toward positive sentiment regarding several CES under the alternative state with variance across demographic and stakeholder groups. Response aggregations in areas of proposed development reveal previously unknown stakeholder preferences to local decision-makers and highlight potential trade-offs for conservation management. Free-listed responses revealed deeper insight into personal opinion and context. 4. This work serves as a useful case study on how the perceptions and opinions of local people regarding local CES could be accounted for in the future planning of an urban greenspace and how thorough analysis of CES provision is important to fully-inform local-scale conservation and planning for the mutual benefit of local communities and nature.

The presented data is a part of a novel, developing approach of data integration for sustainable planning of central activities at the spatial planning level, based on a case study of the Plan of Central Activities for the Municipality of Pecinci in Serbia, published in “Data set on centrality in Municipality of Pecinci, SERBIA”, Mrdjenovic, Tatjana (2024), Mendeley Data, V1, doi: 10.17632/p69pbypd78.1. The current theories and practice of planning central activities in Serbia use mostly functional factors of network flow and graphs; however, this method seeks to improve present practice in data usage towards the sustainable development of central activities and integrative central places using morphological aspects of centrality, i.e., their pattern of local nodality using qualitative and quantitative measures for their development.

The Alphanumeric data in Sheet 1 present raw data on concentration for generating centrality in Municipality of Pecinci. The data was acquired manually by using the number lot as a key for the data on the surface, kind of usage, and type of ownership for each settlement from the official source E-Cadastre https://katastar.rgz.gov.rs/eKatastarPublic/publicaccess.aspx. Both lot number and lot surface were provided at the official base map in CAD format. Alfa numeric data was merged with spatial data later using GIS. All figures and images connected to this data was made using open-source GIS software. The data can be used as a model for measuring centrality in the process of spatial and urban planning.

Urban housing location and locational amenities play an important role in median house price distribution and growth among the suburbs of many metropolitan cities in developed countries, such as Australia. In particular, distance from the central business district (CBD) and access to the transport network plays a vital role in house price distribution and growth over various suburbs in a city. However, Australian metropolitan cities have experienced increases in housing prices by up to 120% over the last 20 years, and the growth pattern was different across all suburbs in a city, such as in Melbourne. Therefore, this study examines the impacts of locational amenities on house price changes across various suburbs in Melbourne over the three census periods of 2006, 2011, and 2016, and suggests some strategic guidelines to improve the availability and accessibility of locational amenities in the suburbs with less concentrated amenities.

This study chose three Local Government Areas (LGAs) of Maribyrnong, Brimbank and Wyndham in Melbourne. Each LGA has been selected as a case study because many low-income people live in these LGAs’ areas. Further, some suburbs of these LGAs have maintained similar housing prices for an extended time, while some have not.

The study applied a quantitative spatial methodology to examine the housing price distribution and growth patterns by evaluating the concentration and accessibility of locational urban amenities using GIS-based techniques and a spatial data set. The spatial data analyses were performed by spatial statistics methods to measure central tendency, Local Moran’s I of LISA clustering, Kernel Density Estimation (KDE), Kernel Density Smoothing (KDS). These tests were used to find the patterns of house price distribution and growth. The study also identified the accessibility of amenities in relation to median house price distribution and growth. Spatial Autoregressive Regression (SAR), Spatial Lag, and Spatial Errors models were used to identify the spatial dependencies to test the statistical significance between the median house price and the concentration and access of local urban amenities over the three census years.

This study found three median house price distribution and growth patterns among the suburbs in the three selected LGAs. There are growth differences in the median house price for different census years between 2006 and 2011, 2011 and 2016, and 2006 and 2016. The Low-High (LH) median house price distribution clusters between 2006 and 2011 became High-High (HH) clusters between the census years 2011 and 2016, and 2006 and 2016. The median house price growth rate increased significantly in the census years between 2006 and 2011. Most of the HH median house price distribution and growth clusters’ tendencies were closer to the Melbourne CBD. On the other hand, the Low-Low (LL) distribution and growth clusters were closer to Melbourne’s periphery. The suburbs located further away had low access to amenities. The HH median house price clusters are located closer to stations and educational institutes. Better access to locational amenities led to more significant HH median house price clusters, as the median house price increased at an increasing rate between 2011 and 2016. The HH median house price clusters recorded more growth between 2006 and 2016. The suburbs with train stations had better access to most other locational amenities. Almost all HH median house price clusters had train stations with higher access to amenities.

There was a consistent relationship between median house price distribution, growth patterns, and locational urban amenities. The spatial lag and spatial error model tests showed that between 2006 and 2011, and 2006 and 2016, there were differences in the amenities. Still, these did not affect the outcomes in observations, and were related only to immeasurable factors for some reason. Therefore, the higher house price in the neighbouring suburb could increase the price in that suburb. The research also found from the regression analysis that highly significant amenities confirming travel time to the CBD by bus, and distance to the CBD, were negatively related in all three previous census years. This negative relationship estimates that the house price growth is lower when the distance is longer. Due to this travel to the CBD by bus is not a popular option for households. The train stations are essential for high house price growth. The house price growth is low when homes are further away from train stations and workplaces.

This thesis has three contributions. Firstly, it uses the Rational Choice Theory (RCT), providing a theoretical basis for analysing households’ mutually interdependent preferences of urban amenities that are found to regulate house price growth clusters. Secondly, the methodological contribution uses the GIS-defined cluster mapping and spatial statistics in queries and reasoning, measurements, transformations, descriptive summaries, optimisation, and hypothesis testing models between house price distribution and growth, and access to urban locational amenities. Thirdly, this research contributes to designing practical guidelines to identify local urban amenities for planning local area development.

Overall, this thesis demonstrates that the median house price distribution and growth patterns are highly correlated with the concentration and accessibility of locational urban amenities among the suburbs in three selected LGAs in Melbourne over the three census years (i.e., 2006, 2011, and 2016). The findings bring to the fore the need for research at the local and state levels to identify specific amenities relevant to the middle-class house distribution strategy, which can be helpful for investors, estate agents, town planners, and builders as partners for effective local development. The future study might use social, psychological, and macroeconomic variables not considered or used in this research.

Unlock precise, high-quality GIS data covering 46M+ verified locations across North America. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

Resilience—the keen ability of people to adapt to changing physical environments—is essential in today's world of unexpected changes.Resilient Communities across Geographies edited by Sheila Lakshmi Steinberg and Steven J. Steinberg focuses on how applying GIS to environmental and socio-economic challenges for analysis and planning helps make communities more resilient.A hybrid of theory and action, Resilient Communities across Geographies uses an interdisciplinary approach to explore resilience studied by experts in geography, social sciences, planning, landscape architecture, urban and rural sociology, economics, migration, community development, meteorology, oceanography, and other fields. Geographies covered include urban and rural, coastal and mountainous, indigenous areas in the United State and Australia, and more. Geographical Information Systems (GIS) is the unifying tool that helped researchers understand resilience.This book shows how GIS:integrates quantitative, qualitative, and spatial data to produce a holistic view of a need for resilience.serves as a valuable tool to capture and integrate knowledge of local people, places, and resources.allows us to visualize data clearly as portrayed in a real-time map or spatial dashboard, thus leading to opportunities to make decisions.lets us see patterns and communicate what the data means.helps us see what resources they have and where they are located.provides a big vision for action by layering valuable pieces of information together to see where gaps are located, where action is needed, or how policies can be instituted to manage and improve community resilience.Resilience is not only an ideal; it is something that people and communities can actively work to achieve through intelligent planning and assessment. The stories shared by the contributing authors in Resilient Communities across Geographies help readers to develop an expanded sense of the power of GIS to address the difficult problems we collectively face in an ever-changing world.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOSSheila Lakshmi Steinberg is a professor of Social and Environmental Sciences at Brandman University and Chair of the GIS Committee, where she leads the university to incorporate GIS across the curriculum. Her research interests include interdisciplinary research methods, culture, community, environmental sociology, geospatial approaches, ethnicity, health policy, and teaching pedagogy.Steven J. Steinberg is the Geographic Information Officer for the County of Los Angeles, California. Throughout his career, he has taught GIS as a professor of geospatial sciences for the California State University and, since 2011, has worked as a geospatial scientist in the public sector, applying GIS across a wide range of both environmental and human contexts.Pub Date: Print: 11/24/2020 Digital: 10/27/2020ISBN: Print: 9781589484818 Digital: 9781589484825Price: Print: $49.99 USD Digital: $49.99 USDPages: 350 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1. Conceptualizing spatial resilience Dr. Sheila Steinberg and Dr Steven J. SteinbergChapter 2. Resilience in coastal regions: the case of Georgia, USAChapter 3. Building resilient regions: Spatial analysis as a tool for ecosystem-based climate adaptationChapter 4. The mouth of the Columbia River: USACE, GIS and resilience in a dynamic coastal systemChapter 5. Urban resilience: Neighborhood complexity and the importance of social connectivityChapter 6. Mapping Indigenous LAChapter 7. Indigenous Martu knowledge: Mapping place through song and storyChapter 8. Developing resiliency through place-based inquiry in CanadaChapter 9. Engaging Youth in Spatial Modes of Thought toward Social and Environmental ResilienceChapter 10. Health, Place, and Space: Public Participation GIS for Rural Community PowerChapter 11. Best Practices for Using Local KnowledgeContributorsIndex

The Cloud GIS market is experiencing robust growth, projected to reach a substantial value with a Compound Annual Growth Rate (CAGR) of 14% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing need for real-time data processing and analysis across various sectors, including urban planning, environmental management, and logistics, is fueling demand for cloud-based Geographic Information Systems (GIS). The scalability and cost-effectiveness offered by cloud platforms, compared to on-premise solutions, are significant advantages attracting businesses of all sizes. Furthermore, advancements in cloud computing technologies, such as improved storage capacity, enhanced processing power, and advanced analytics capabilities, are accelerating market adoption. The integration of AI and machine learning within Cloud GIS platforms is also a major contributor, enabling sophisticated spatial analysis and predictive modeling. Competition among leading providers like Esri, Hexagon, and Mapbox is intense, focusing on developing innovative solutions, expanding partnerships, and strengthening customer engagement through user-friendly interfaces and comprehensive support services. Geographical expansion, particularly in developing economies with increasing digital infrastructure, further contributes to market growth. However, data security concerns and the reliance on stable internet connectivity remain potential restraints. The market segmentation reveals a diverse landscape. The "Type" segment likely includes various cloud deployment models (e.g., public, private, hybrid), each catering to specific organizational needs and security requirements. The "Application" segment is equally broad, encompassing diverse use cases like smart city initiatives, precision agriculture, disaster response management, and infrastructure development. North America currently holds a significant market share due to early adoption and a mature technological landscape, but the Asia-Pacific region is expected to witness rapid growth driven by increasing urbanization and infrastructure investments. The competitive landscape is dynamic, with companies focusing on strategic partnerships, acquisitions, and continuous product innovation to maintain a leading position. Future growth will be largely influenced by the expansion of 5G networks, the continued advancement of AI/ML in spatial analysis, and the increasing availability of high-resolution geospatial data.

Background: The transformation of public spaces in urban contexts requires a nuanced understanding of the interplay between architecture, cultural identity, and community needs. This study examines the Haliç Metro Köprüsü (Golden Horn Metro Bridge) in Istanbul, a structure that serves as both a critical piece of infrastructure and a contested public space within the city’s historical landscape. Positioned at the intersection of modernist design and the Golden Horn’s rich cultural heritage, the bridge has sparked debates about its impact on Istanbul's urban identity and public space. Methods: This study employs a case study approach to explore the Haliç Metro Köprüsü as a public space, using spatial analysis, stakeholder interviews, and archival research. Spatial analysis uses GIS and site observations to examine the bridge's physical characteristics, while stakeholder interviews gather perspectives on its functionality and cultural significance. Archival research reviews historical and policy documents to understand the socio-political context of the bridge's development. Findings: The study considers the challenges of reconciling the bridge’s functional role with the preservation of cultural authenticity, while also addressing the need for inclusive urban spaces that reflect the city’s diverse communities. Through a combination of spatial analysis, stakeholder perspectives, and theoretical frameworks on place identity, the research highlights strategies for reclaiming the bridge as a vibrant and culturally resonant public space. Findings emphasize the importance of integrating place-making principles into urban design processes, particularly in heritage-sensitive contexts. Conclusion: This approach not only enhances the usability and aesthetic value of public spaces but also strengthens their role as mediums for cultural expression and identity formation. Novelty/Originality of this article: The study contributes to ongoing discussions on urban authenticity and the evolving relationship between architecture, public space, and place identity.

This dataset supports the development of the Extreme Weather Vulnerability Index (EWVI), integrated into a decision support model combining Geographical Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA). Using the City of Joensuu in eastern Finland as a case study, the dataset incorporates remotely sensed data and building-level information to categorize neighbourhoods based on their energy consumption potential.

The EWVI aims to identify areas that are vulnerable to extreme weather events, emphasizing the need for targeted interventions, such as building renovations or greening initiatives. This dataset includes key inputs for assessing the energy usage potential and vulnerabilities of urban areas, offering valuable insights for practitioners, policymakers, and researchers.

This tool can aid in decision-making at both the building and neighbourhood scales, supporting more resilient urban planning and climate adaptation strategies.

Note: The article associated with this dataset is currently under review.

This research study analysed the crime rate spatially and it examined the relationship between crime and spatial factors in Saudi Arabia. It reviewed the related literature that has utilised crime mapping techniques, such as Geographic Information Systems (GIS) and remote sensing (RS); these techniques are a basic part of effectively helping security and authority agencies by providing them with a clear perception of crime patterns and a surveillance direction to track and tackle crime. This study analysed the spatial relationships between crime and place, immigration, changes in urban areas, weather and transportation networks. The research study was divided into six parts to investigate the correlation between crime and these factors. The first part of the research study examined the relationship between crime and place across the 13 provinces of Saudi Arabia using GIS techniques based on population density in order to identify and visualise the spatial distributions of national and regional crime rates for drug crimes, thefts, murders, assaults, and alcohol-related and ‘outrageous crimes’ (offences against Islam) over a 10-year period from 2003 to 2012. Social disorganisation theory was employed to guide the study and explain the diversity in crime patterns across the country. The highest rates of overall crimes were identified in the Northern Borders Province and Jizan, which are located in the northern and southern regions of the country, respectively; the eastern area of the country was found to have the lowest crime rate. Most drug offences occurred in the Northern Borders Province and Jizan; high rates of theft were recorded in the Northern Borders Province, Jouf Province and Makkah Province, while the highest rates of homicide occurred in Asir Province. The second part of the research study aimed to determine the trends of overall crime in relation to six crime categories: drug-related activity, theft, murder, assault, alcohol-related crimes and outrageous or sex-related crimes, in Saudi Arabia’s 13 provinces over a 10-year period from 2003 to 2012. The study analysed the spatial and temporal changes of criminal cases. Spatial changes were used to determine the differences over the time period of 2003–2012 to show the provincial rates of change for each crime category. Temporal changes were used to compute the trends of the overall crime rate and crimes in the six categories per 1,000 people per year. The results showed that the overall crime rate increased steadily until 2008; thereafter it decreased in all areas except for the Northern Borders Province and Jizan, which recorded the highest crime rates throughout the study period. We have explained that decrease in terms of changes in wages, support for the unemployed and service improvements, which were factors that previous studies also emphasised as being the primary cause for the decrease. This study includes a detailed discussion to contribute to the understanding of the changes in the crime rates in these categories throughout this period in the 13 provinces of Saudi Arabia. The third part of the research study aimed to explain the effects of immigration on the overall crime rate in the six most significant categories of crime in Saudi Arabia, which are drug-related activity, theft, murder, assault, alcohol-related crimes and outrageous crimes, during a 10-year period from 2003 to 2012, in all 13 administrative provinces. It also sought to identify the provinces most affected by the criminal activities of immigrants during this period. No positive association between immigrants and criminal cases was found. It was clearly visible that the highest rate of overall criminal activities was in the south, north and Makkah areas, where there is a high probability of illegal immigrants. This finding supports the basic criminological theory that areas with high levels of immigrants also experience high rates of crime. The study’s results provide recommendations to the Saudi government, policy-makers, decision-makers and immigration authorities, which could assist in reducing crimes perpetrated by immigrants. In the fourth part of the research study, urban areas were examined in relation to crime rates. Urban area expansion is one of the most critical types of worldwide change, and most urban areas are experiencing increased population growth and infrastructure development. Urban change leads to many changes in the daily activities of people living within an affected area. Many studies have suggested that urbanisation and crime are related. However, those studies focused on land uses, types of land use and urban forms, such as the physical features of neighbourhoods, roads, shopping centres and bus stations. It is very important for criminologists and urban planning decision-makers to understand the correlation between urban area expansion and crime. In this research, satellite images were used to measure urban expansion over a 10-year period; the study tested the correlations between these expansions and the number of criminal activities within these specific areas. The results show that there is a measurable relationship between urban expansion and criminal activities. The findings support the crime opportunity theory as one possibility, which suggests that population density and crime are conceptually related. Moreover, the results show that the correlations are stronger in areas that have undergone greater urban growth. This study did not evaluate many other factors that might affect the crime rate, such as information on the spatial details of the population, city planning, economic considerations, the distance from the city centre, the quality of neighbourhoods, and the number of police officers. However, this research will be of particular interest to those who aim to use remote sensing to study crime patterns. The fifth part of the research study investigated the impacts of weather on crime rates in two different cities: Riyadh and Makkah. While a number of studies have examined climate influences on crime and human behaviour by investigating the correlation between climate and weather elements, such as temperature, humidity and precipitation, and crime rates, few studies have focused on haze as a weather element and its correlation with crime. This research examined haze as a weather variable to investigate its effects on criminal activity and compare its effects with those of temperature and humidity. Monthly crime data and monthly weather records were used to build a regression model to predict crime cases based on three weather factors using temperature, humidity and haze values. This model was applied to two provinces in Saudi Arabia with different types of climates: Riyadh and Makkah. Riyadh Province is a desert area in which haze occurs approximately 17 days per month on average. Makkah Province is a coastal area where it is hazy an average of 4 days per month. A measurable relationship was found between each of these three variables and criminal activity. However, haze had a greater effect on theft, drug-related crimes and assault in Riyadh Province than temperature and humidity. Temperature and humidity were the efficacious variables in Makkah Province, while haze had no significant influence in that region. Finally, the sixth part of the research study examined the influence of the quality and extent of road networks on crime rates in both urban and rural areas in Jizan Province, Saudi Arabia. We performed both Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR) where crime rate was the dependent variable and paved (sealed) roads, non-paved (unsealed/gravel) roads and population density were the explanatory variables. Population density was a control variable. The findings reveal that, across all 14 districts in that province, the districts with better quality paved road networks had lower rates of crime than the districts with unpaved roads. Furthermore, the more extensive the road networks, the lower the crime rate whether or not the roads were paved. These findings concur with those reported in studies conducted in other countries, which revealed that rural areas are not always the safe, crime-free places they are often believed to be. This research contributes knowledge about the geographical information of criminal movement, and it offers some conceivable reasons for crime rates and patterns in relation to the spatial factors and the socio-cultural perspectives of Saudi Arabian life. More geographical research is still needed in terms of criminology, which will provide a better understanding of crime patterns, particularly in Saudi Arabia, and across the globe, where the spatial distribution of criminal cases is an essential base in crime research. Furthermore, additional studies are needed to investigate the complex interventions of the effect of different spatial variables on crime and the uncertainties correlation with the impact of environmental factors. This can help predict the impact of socioeconomic and environmental factors. The greater part of such an investigation will enhance the understanding of crime patterns, which is imperative for advancing a framework that can be used to address crime reduction and crime prevention.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.2839 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.026 and 0.0089 (in million kms), corressponding to 9.1664% and 3.1261% respectively of the total road length in the dataset region. 0.249 million km or 87.7075% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0003 million km of information (corressponding to 0.1046% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Unlock precise, high-quality GIS data covering 6.1M+ verified locations across Germany. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

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Unlock precise, high-quality GIS data covering 9M+ verified locations across Latin America. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.